Speed Profile Prediction in Intelligent Transport Systems Exemplified by Vehicle to Vehicle Interactions

Bosankic

et al. 2016

PLoS ONE

Self Cite

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder

Shamshirband

Bosankic

et al. 2016

PLoS ONE

Self Cite

“…The predictive techniques are being accomodated to allow advanced nonlinear models using historical baseline traffic data, such as speed, flow and travel time. For traffic information prediction, between numerous nonparametric prediction approaches most often are used artificial neural networks (ANN), support vector regression (SVR), and the adaptive neurofuzzy system (ANFIS)) [15][16][17][18]. The main advantage of the artificial neural network (ANN) is its ability to model very complex multivariable systems, and the quality of prediction is tuned and improved by parameters of the network such as the number of hidden neurons and learning factor [19].…”

Section: Introductionmentioning

confidence: 99%

“…The main advantage of the artificial neural network (ANN) is its ability to model very complex multivariable systems, and the quality of prediction is tuned and improved by parameters of the network such as the number of hidden neurons and learning factor [19]. The neural network prediction of the speed profile in transport systems is presented in [15][16]. Similar approach in modelling is with SVR techniques where the predictive model relies heavily on proper determination of model parameters [17].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Automaton Based Vehicle Platoon Modelling and Cooperation Behaviour Profile Prediction

Butigan²,

Mehmedović³

et al. 2018

Teh. vjesn.

Autonomous cooperative driving systems require the integration of research activities in the field of embedded systems, robotics, communication, control and artificial intelligence in order to create a secure and intelligent autonomous drivers behaviour patterns in the traffic. Beside autonomous vehicle management, an important research focus is on the cooperation behaviour management. In this paper, we propose hybrid automaton modelling to emulate flexible vehicle Platoon and vehicles cooperation interactions. We introduce novel coding function for Platoon cooperation behaviour profile generation in time, which depends of vehicles number in Platoon and behaviour types. As the behaviour prediction of transportation systems, one of the primarily used methods of artificial intelligence in Intelligent Transport Systems, we propose an approach towards NARX neural network prediction of Platoon cooperation behaviour profile. With incorporation of Platoon manoeuvres dynamic prediction, which is capable of analysing traffic behaviour, this approach would be useful for secure implementation of real autonomous vehicles cooperation.

“…The neural network prediction of the speed profile is presented in [11], [12]. The speed profile neural network based prediction for selected agents using vehicle-tovehicle (V2V) communication is presented in [13].…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Prediction of Cooperation Interaction Profile of Flexible Road Train Based on Hybrid Automaton Modeling

Delic²,

Butigan³

et al. 2016

MATEC Web Conf.

Self Cite

Abstract. Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS). The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS) prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error), coefficient of determination (R2) and Pearson coefficient (r), were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.